Applications of Cultural Ant Colony Optimization in Optimal Excavator Mechanisms Design

2012 ◽  
Vol 479-481 ◽  
pp. 1857-1862
Author(s):  
Pei Qing Xie ◽  
Shu Wen Lin
Keyword(s):  
Design Optimization ◽  
Ant Colony Optimization ◽  
Engineering Design ◽  
Optimization Algorithms ◽  
Ant Colony ◽  
Optimization Process ◽  
Unsatisfactory Result ◽  
Engineering Design Optimization ◽  
Low Efficiency

In allusion to the low efficiency and unsatisfactory result of the tradional optimization algorithms in existence for engineering design optimization,this paper proposes a cultural ant colony optimization(CACO) algorithm for application in design optimization of excavator’s mechanisms to improve the excavator’s performance efficiently. Through testing and verifying experiments,it is concluded that CACO can discovery knowledge during optimization process and use the knowledge to guide the heuristic searching process,furthermore,it is an appropriate algorithm for the optimization of excavator mechanisms. CACO costs less time and can get better quality solution to improve excavator’s main porformances.

A Fuzzy Sequential Linear Programming Algorithm for Engineering Design Optimization

1995 ◽  
Author(s):  
Yeh-Liang Hsu ◽  
Yu-Fa Lin ◽  
Yu-Shuei Guo
Keyword(s):  
Linear Programming ◽  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Optimization Process ◽  
Programming Algorithm ◽  
Sequential Linear Programming ◽  
Original Algorithm ◽  
Engineering Design Optimization

Abstract An optimization process can be viewed as a closed-loop control system. Traditional “controllers”, the numerical optimization algorithms, are usually “crisply” designed for well defined mathematical models. However, when applied to engineering design optimization problems in which function evaluations can be expensive and imprecise, very often the crisp algorithms will become impractical or will not converge. A common strategy for designers is to monitor the optimization process and keep “tuning” the process in an interactive manner, using their judgment on the information obtained from previous iterations, and their knowledge of the problem. This paper presents how the heuristics of this human supervision can be modeled into the optimization algorithms using fuzzy set theory. A fuzzy version of sequential linear programming is used to demonstrate this idea. Fuzzy rules, which describe the human supervision during the optimization process, are combined with the numerical rules of the original algorithm to refine the output of each iteration. Several design optimization problems are used to show the feasibility and practicality of this approach.

Gear Design Optimization Algorithms: A Review

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
YO Usman ◽  
PO Odion ◽  
EO Onibere ◽  
AY Egwoh
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Design Parameters ◽  
Gear Design ◽  
Complex Design

Gearing is one of the most efficient methods of transmitting power from a source to its application with or without change of speed or direction. Gears are round mechanical components with teeth arranged in their perimeter. Gear design is complex design that involves many design parameters and tables, finding an optimal or near optimal solution to this complex design is still a major challenge. Different optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing, Ant-Colony Optimization, and Neural Network etc., have been used for design optimization of the gear design problems. This paper focuses on the review of the optimization techniques used for gear design optimization with a view to identifying the best of them. Nowadays, the method used for the design optimization of gears is the evolutionary algorithm specifically the genetic algorithm which is based on the evolution idea of natural selection. The study revealed that GA. has the ability to find optimal solutions in a short time of computation by making a global search in a large search space. Keywords: Firefly Algorithm, Ant-Colony Optimization, Simulated Annealing, Genetic Algorithm, Gear design, Optimization, Particle Swarm Optimization Algorithm

scholarly journals Machine–Learning in Optimization of Expensive Black–Box Functions

2017 ◽  
Vol 27 (1) ◽  
pp. 105-118 ◽  
Author(s):  
Yoel Tenne
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Engineering Design ◽  
Computer Simulations ◽  
Numerical Experiments ◽  
Black Box ◽  
Optimization Process ◽  
Modern Engineering ◽  
Engineering Design Optimization ◽  
Unknown Reason

Abstract Modern engineering design optimization often uses computer simulations to evaluate candidate designs. For some of these designs the simulation can fail for an unknown reason, which in turn may hamper the optimization process. To handle such scenarios more effectively, this study proposes the integration of classifiers, borrowed from the domain of machine learning, into the optimization process. Several implementations of the proposed approach are described. An extensive set of numerical experiments shows that the proposed approach improves search effectiveness.

Automated Fault Tree Analysis for Engineering Design Optimization

2007 ◽  
Author(s):  
Tiefu Shao ◽  
Zongfang Lin ◽  
Sundar Krishnamurty ◽  
Ian R. Grosse ◽  
Leon J. Osterweil
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Process Model ◽  
Autonomous Agents ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Optimization Process ◽  
Tree Analysis ◽  
Use Of Resources ◽  
Engineering Design Optimization

This paper presents an automated fault tree analysis for engineering design optimization process. Specifically, a novel approach is presented in which Little-JIL, a process programming language, is applied to create a process model of engineering optimization. The process model uses a graphical language in the form of easy-to-understand block diagrams for defining processes that coordinate the activities of autonomous agents and their use of resources during the performance of a task. The use of Little-JIL facilitates agent coordination in the design optimization process and helps to model the order of and the communications between units of sub-processes. The resulting process model is easy to debug and is rigorous for simulation and formal reasoning in engineering design optimization. Furthermore, it enables the development of a clear and precise design optimization process model at different levels of granularity as perhaps preferred by the user. Moreover, since the process model allows for generation of fault trees automatically, it can be expected to be less errorprone than manually generated ones. A case study is shown to demonstrate the effectiveness and efficiency of the automated fault tree approach to design optimization and its usefulness in engineering decision making and in improving reliability of engineering design process.

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